A noteworthy and innovative article previously disseminated in the RBF Bulletin as a White Paper has now been published by peer-reviewed journal Plos One. In this article, authors Grover, Bauhoff and Friedman find that machine learning techniques (using a Random Forest approach) can be used to identify audit targets and thereby reduce costs of verification—a critical (and somewhat expensive) element of results-based financing.

Independent verification is a critical component of performance-based financing (PBF) in health care, in which facilities are offered incentives to increase the volume of specific services but the same incentives may lead them to over-report. We examine alternative strategies for targeted sampling of health clinics for independent verification. Specifically, we empirically compare several methods of random sampling and predictive modeling on data from a Zambian PBF pilot that contains reported and verified performance for quantity indicators of 140 clinics.

Training, motivating and retaining human resources is crucial for the improvement of health outcomes, especially in low and middle-income countries (LMICs) where human resources availability and management have been recognized as one of the key health system’s barriers to the achievement of the Millennium Development Goals. In recognition of the limitations of current financial incentives and/or remuneration levels, Performance-Based Financing (PBF) mechanisms have been introduced in many LMICs in recent years.

Results-based financing (RBF) has been implemented in low- and middle-income countries with the aim of transforming health systems and achieving Millennium Development Goals 4 and 5. However, there is a dearth of empirical research on the impact of RBF-facility financing and provider incentives on performance related factors such as health workers satisfaction, motivation, productivity, and retention. This paper attempts to fill this gap by examining the relationship between RBF and health care practitioner outcomes through the case of Zambia.

Dr. Robert Soeters, an independent public health and health financing specialist, shares a personal story about performance-based financing.

Soeters explores the PBF programs he has witnessed in countries such as Burundi, Cambodia, the Democratic Republic of Congo and Rwanda.

Un programa innovador, que autoriza a los centros de salud a gastar recursos en lo que más necesitan, permite que los bebés y las madres estén saludables en Zambia. El Fondo Fiduciario para Innovación en materia de Resultados en el Sector de la Salud del Banco Mundial (HRITF, por sus siglas en inglés), que es respaldado por los Gobiernos de Noruega y el Reino Unido, ha apoyado estrategias de financiamiento basado en resultados (RBF, por sus siglas en inglés) desde 2007.

This video puts the spotlight on rural Zambia, where the results-based financing program launched in April 2012.

Early results show progress in health outcomes for babies and their mothers: increases in the utilization of reproductive health services; the number of deliveries by skilled personnel; the number of first ANC visits in RBF facilities; and in total family planning attendance.

Several developing countries face the challenge of attaining sufficient population-level impact to meet health-related Millennium Development Goals (MDGs) 1c, 4, 5, and 6. 1 This situation is partly attributable to constraints in their health systems, including: severe shortages in human resources for health; inequalities in service provision and utilization; limited financial resources; and inefficiencies in resource allocation and use.

This presentation was given at the 2nd Global Symposium on Health Systems Research in Beijing, China, November 1, 2012 during the session "Approaches to Embedding Research in Action".

This page contains links to the detailed designs of the HRITF-funded Impact Evaluations (those that are designed and approved).

Each country's sheet contains the date of last update.

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